Except for the GDFQ method, we use the GFAD[1]} method as a baseline to show the generalization of our AutoReCon method. The generation loss in the GFAD method is replaced with the reconstruction loss of Equation 6, which enables the exploration of generator architecture. We use ResNet34 as the pre-trained teacher model and ResNet18 as the student model. The experimental results of data-free knowledge distillation on CIFAR-100 is shown in Table REF . With a human-designed generator, the GFAD method achieves better accuracy than the DAFL[2]} method. The Top-1 accuracy of our data-free knowledge distillation with a discovered generator is \(2.28\%\)  better than the baseline of the GFAD method with a human-designed generator.
<TABLE>